Traditional methods for character recognition carry out recognition based on binary character images. When recognizing various low-quality images, for example, the low-resolution image, the image of an identification card, an automobile license plate and a image of natural scenery, because their binarized images are extremely low quality, traditional methods for character recognition present a poor performance in recognizing characters regarding above-mentioned images.
Because of the disadvantages of traditional methods for character recognition, many prior arts does not carry out recognition based on binarization, but directly extract recognition features from a gray character image.
To directly extract recognition features from a gray character image, two specific methods are as follows:
One method extracts topographical and gradient features, etc. by means of analyzing the gradient distribution of a gray character image. Because the distribution of image's brightness needs to meet with some specific rules, the method owns a poor capability to resist noises.
The other method extracts recognition features by means of simple orthogonal transformation, for example, FFT or DCT Because the transformations reflect only global information of a character image, the method cannot extract local stroke direction and structural information of the character image, resulting in a poor capability to resist to the change in image's brightness and the deformation of the character.
Gabor filter possesses excellent joint spatial/spatial-frequency localization and capability to efficiently extract local structural features, thus, the method emerges at present to employ Gabor filters to extract the recognition features of handwritten digital images. However, current methods have following disadvantages:
When designing the Gabor filters, because the method selects different parameter values on the basis of different recognition ratios, the procedure of the method is tedious and the amount of calculation is tremendous, resulting in a poor capability to recognize.
The method does not apply Gabor filter to gray character image of low quality to carry out the recognition procedure, but apply Gabor filter only to binary image to recognize the characters. Therefore, when the method is applied to gray character image of low quality, the capability to discriminate the strokes off backgrounds of character images is poor.
Upon the above, though Gabor filter is used for character recognition, the method does not make full use of Gabor filter's excellent joint spatial/spatial-frequency localization and capability to efficiently extract local structural features.